Parallelization and Performance Evaluation of an Edge Detection Algorithm on a Streaming Multi-Core Engine

Edge Detection

Detecting edges is a fundamental application in image processing and early computer vision. The edges of objects appearing in an image hold much of the information in the image. The image edges give the locations of where items are, their size, shape, and something about their texture. An edge is where the gray level of the image moves from an area of low values to high values or vice versa (Canny, 1986; Deriche, 1987; Lindeberg, 1998; Lindeberg 1994; Zhang & Bergholm. 1997; Ziou & Tabbone, 1998). The edge itself is at the center of this transition. An edge detector should return an image with gray levels like those shown in the lower part of Figure 1. An edge is defined by a discontinuity in gray-level values.

Figure 1.

Graphs of gray scale values at an edge. (Top) A single row from an image representing an edge. (Bottom) Result of an edge detection operator applied to edge of (Top)

To illustrate why edge detection is a non-trivial task, let us consider the problem of detecting edges in the one-dimensional signal of Figure 2. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels due to the large difference in values between the gray level intensity of pixel 4 (4) and pixel 5 (152). If the intensity difference was smaller between the 4^th and 5^th pixels, it would not be as easy to say that there should be an edge in this corresponding region (Wikipedia).

Figure 2.

Gray level values of a one-dimensional edge. (Courtesy wikipedia.org)

The detected edge is a bright spot at the edge and dark areas everywhere else. Edge detection is often referred to as image differentiation.